Rostlab/prot_bert · Hugging Face
Model Card for Zephyr 7B β
Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that removi... See more
Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that removi... See more
HuggingFaceH4/zephyr-7b-beta · Hugging Face
Nicolay Gerold added
koboldcpp
🤗 Transformers
Huggingface is an open source platform and community for deep learning models for language, vision, audio and multimodal. They develop and maintain the transformers library, which simplifies the process of downloading and training state of the art deep learning models.
This is the best library if you have a background in m... See more
🤗 Transformers
Huggingface is an open source platform and community for deep learning models for language, vision, audio and multimodal. They develop and maintain the transformers library, which simplifies the process of downloading and training state of the art deep learning models.
This is the best library if you have a background in m... See more
Moyi • 10 Ways To Run LLMs Locally And Which One Works Best For You
Nicolay Gerold added
Text embeddings are a critical piece of many pipelines, from search, to RAG, to vector databases and more. Most embedding models are BERT/Transformer-based and typically have short context lengths (e.g., 512). That’s only about two pages of text, but documents can be very long – books, legal cases, TV screenplays, code repositories, etc can be tens... See more
Long-Context Retrieval Models with Monarch Mixer
Nicolay Gerold added
Protein
protein.xyzMo Shafieeha added
Although there are already many methods available for keyword generation (e.g., Rake, YAKE!, TF-IDF, etc.) I wanted to create a very basic, but powerful method for extracting keywords and keyphrases. This is where KeyBERT comes in! Which uses BERT-embeddings and simple cosine similarity to find the sub-phrases in a document that are the most simila... See more
MaartenGr • GitHub - MaartenGr/KeyBERT: Minimal keyword extraction with BERT
Nicolay Gerold added
Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-α is the first model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that removing the in-built alignment o... See more
HuggingFaceH4/zephyr-7b-alpha · Hugging Face
Nicolay Gerold added
Welcome to RAGatouille
Easily use and train state of the art retrieval methods in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.
The main motivation of RAGatouille is simple: bridging the gap between state-of-the-art research and alchemical RAG pipeline practices. RAG is complex, and there are many moving parts. To g... See more
Easily use and train state of the art retrieval methods in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.
The main motivation of RAGatouille is simple: bridging the gap between state-of-the-art research and alchemical RAG pipeline practices. RAG is complex, and there are many moving parts. To g... See more
GitHub - bclavie/RAGatouille: Easily use and train state of the art late-interaction retrieval methods (ColBERT) in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.
Nicolay Gerold added
ColBERT is a
fast
and
accurate
retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds.
Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage.
As Figure 1 illustrates, ColBERT relies on fine-grained contextual late interaction : it encod... See more
fast
and
accurate
retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds.
Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage.
As Figure 1 illustrates, ColBERT relies on fine-grained contextual late interaction : it encod... See more
stanford-futuredata • GitHub - stanford-futuredata/ColBERT: Stanford ColBERT: state-of-the-art neural search (SIGIR'20, TACL'21, NeurIPS'21, NAACL'22, CIKM'22)
Nicolay Gerold added